Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach

TL;DR

This paper explores how persuasive and deceptive language in business communication can be systematically detected using computational methods, achieving high accuracy in controlled settings and highlighting challenges in multilingual contexts.

Contribution

It combines classical rhetoric, psychology, and linguistic theory with empirical analysis to develop effective textual detection methods for deceptive language in business discourse.

Findings

Detection accuracy >99% in controlled settings

Challenges in multilingual detection due to data scarcity

Need for robust AI-based discourse analysis systems

Abstract

Business communication digitisation has reorganised the process of persuasive discourse, which allows not only greater transparency but also advanced deception. This inquiry synthesises classical rhetoric and communication psychology with linguistic theory and empirical studies in the financial reporting, sustainability discourse, and digital marketing to explain how deceptive language can be systematically detected using persuasive lexicon. In controlled settings, detection accuracies of greater than 99% were achieved by using computational textual analysis as well as personalised transformer models. However, reproducing this performance in multilingual settings is also problematic and, to a large extent, this is because it is not easy to find sufficient data, and because few multilingual text-processing infrastructures are in place. This evidence shows that there has been an increasing gap between the theoretical representations of communication and those empirically approximated, and therefore, there is a need to have strong automatic text-identification systems where AI-based discourse is becoming more realistic in communicating with humans.